Running ‘testthat.R’ [178s/189s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(ggeffects)
  > 
  > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
  +   Sys.setenv("RunAllggeffectsTests" = "yes")
  + } else {
  +   Sys.setenv("RunAllggeffectsTests" = "no")
  + }
  > 
  > test_check("ggeffects")
  GAM s.wam loop 1: deviance = 66.42095 
  GAM s.wam loop 2: deviance = 63.77252 
  GAM s.wam loop 3: deviance = 63.25199 
  GAM s.wam loop 4: deviance = 63.13399 
  GAM s.wam loop 5: deviance = 63.11016 
  GAM s.wam loop 6: deviance = 63.10748 
  GAM s.wam loop 7: deviance = 63.10727 
  GAM s.wam loop 8: deviance = 63.10725 
  GAM s.wam loop 9: deviance = 63.10725 
  ── 1. Failure: ggemmeans (@test-coxph.R#19)  ───────────────────────────────────
  p$predicted[1] not equal to 0.5622074.
  1/1 mismatches
  [1] 0.752 - 0.562 == 0.19
  
  (Intercept)    tensionM    tensionH 
     36.38889   -10.00000   -14.72222 
  Can't compute marginal effects, 'effects::Effect()' returned an error.
  
  Reason: object 'neg_c_7d' not found
  You may try 'ggpredict()' or 'ggemmeans()'.
  
  Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
  
  Reason: Can't handle an object of class  "glmrob" 
   Use help("models", package = "emmeans") for information on supported models.
  You may try 'ggpredict()' or 'ggeffect()'.
  
  
  # Predicted values of Total score BARTHEL INDEX
  # x = average number of hours of care per week
  
  #  c161sex = Male
  # c172code = [1] low level of education
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     73.95 | 2.35 | [69.35, 78.55]
   45 |     62.56 | 2.21 | [58.23, 66.88]
   85 |     52.42 | 2.31 | [47.90, 56.95]
  170 |     30.89 | 3.08 | [24.85, 36.94]
  
  #  c161sex = Female
  # c172code = [1] low level of education
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     75.00 | 1.83 | [71.41, 78.59]
   45 |     63.60 | 1.60 | [60.46, 66.74]
   85 |     53.46 | 1.70 | [50.13, 56.80]
  170 |     31.93 | 2.61 | [26.83, 37.04]
  
  #  c161sex = Male
  # c172code = [2] intermediate level of education
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     74.67 | 1.85 | [71.06, 78.29]
   45 |     63.27 | 1.73 | [59.88, 66.66]
   85 |     53.14 | 1.91 | [49.40, 56.89]
  170 |     31.61 | 2.87 | [25.98, 37.24]
  
  #  c161sex = Female
  # c172code = [2] intermediate level of education
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     75.71 | 1.23 | [73.31, 78.12]
   45 |     64.32 | 0.97 | [62.42, 66.21]
   85 |     54.18 | 1.21 | [51.81, 56.55]
  170 |     32.65 | 2.40 | [27.94, 37.36]
  
  #  c161sex = Male
  # c172code = [3] high level of education
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     75.39 | 2.22 | [71.04, 79.74]
   45 |     63.99 | 2.18 | [59.73, 68.26]
   85 |     53.86 | 2.36 | [49.23, 58.49]
  170 |     32.33 | 3.26 | [25.95, 38.71]
  
  #  c161sex = Female
  # c172code = [3] high level of education
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     76.43 | 1.81 | [72.89, 79.98]
   45 |     65.03 | 1.71 | [61.68, 68.39]
   85 |     54.90 | 1.91 | [51.16, 58.65]
  170 |     33.37 | 2.89 | [27.70, 39.05]
  
  Adjusted for:
  * neg_c_7 = 11.84
  
  
  # Predicted values of Total score BARTHEL INDEX
  # x = average number of hours of care per week
  
  # c161sex = Male
  # neg_c_7 = 11.8
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     74.74 | 1.85 | [71.12, 78.36]
   45 |     63.34 | 1.73 | [59.95, 66.73]
   85 |     53.21 | 1.91 | [49.46, 56.95]
  170 |     31.68 | 2.87 | [26.05, 37.30]
  
  # c161sex = Female
  # neg_c_7 = 11.8
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     75.78 | 1.23 | [73.38, 78.18]
   45 |     64.38 | 0.97 | [62.49, 66.28]
   85 |     54.25 | 1.21 | [51.89, 56.61]
  170 |     32.72 | 2.40 | [28.01, 37.42]
  
  # c161sex = Male
  # neg_c_7 = 15.7
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     65.78 | 2.17 | [61.54, 70.02]
   45 |     54.38 | 1.98 | [50.50, 58.26]
   85 |     44.25 | 2.06 | [40.20, 48.29]
  170 |     22.72 | 2.86 | [17.11, 28.33]
  
  # c161sex = Female
  # neg_c_7 = 15.7
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     66.82 | 1.59 | [63.70, 69.94]
   45 |     55.42 | 1.27 | [52.94, 57.91]
   85 |     45.29 | 1.35 | [42.65, 47.93]
  170 |     23.76 | 2.34 | [19.18, 28.34]
  
  # c161sex = Male
  # neg_c_7 = 8
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     83.47 | 1.91 | [79.72, 87.21]
   45 |     72.07 | 1.89 | [68.36, 75.78]
   85 |     61.94 | 2.13 | [57.76, 66.11]
  170 |     40.41 | 3.13 | [34.28, 46.54]
  
  # c161sex = Female
  # neg_c_7 = 8
  
    x | Predicted |   SE |         95% CI
  ---------------------------------------
    0 |     84.51 | 1.41 | [81.75, 87.27]
   45 |     73.11 | 1.33 | [70.51, 75.71]
   85 |     62.98 | 1.61 | [59.83, 66.13]
  170 |     41.45 | 2.75 | [36.07, 46.83]
  
  Adjusted for:
  * c172code = 1.97
  
  Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
  
  Reason: Can't handle an object of class  "lmrob" 
   Use help("models", package = "emmeans") for information on supported models.
  You may try 'ggpredict()' or 'ggeffect()'.
  
  Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
  
  Reason: Can't handle an object of class  "logistf" 
   Use help("models", package = "emmeans") for information on supported models.
  You may try 'ggpredict()' or 'ggeffect()'.
  
  Can't compute marginal effects, 'effects::Effect()' returned an error.
  
  Reason: object 'neg_c_7d' not found
  You may try 'ggpredict()' or 'ggemmeans()'.
  
  Error: Confidence intervals could not be computed.
  * Reason: "`Type` does not have enough factor levels. Try to remove `[Tower]`."
  * Source: .safe_se_from_vcov(model, prediction_data, value_adjustment, terms, model_class, type, vcov.fun, vcov.type, vcov.args, condition, interval)
  Error: Confidence intervals could not be computed.
  * Reason: "`Type` does not have enough factor levels. Try to remove `[Terrace]`."
  * Source: .safe_se_from_vcov(model, prediction_data, value_adjustment, terms, model_class, type, vcov.fun, vcov.type, vcov.args, condition, interval)
  Error: Confidence intervals could not be computed.
  * Reason: "`Type` does not have enough factor levels. Try to remove `[Tower]`."
  * Source: .safe_se_from_vcov(model, prediction_data, value_adjustment, terms, model_class, type, vcov.fun, vcov.type, vcov.args, condition, interval)
  Can't compute marginal effects, 'effects::Effect()' returned an error.
  
  Reason: object does not have variance-covariance matrix
  You may try 'ggpredict()' or 'ggemmeans()'.
  
  Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
  
  Reason: Can't handle an object of class  "rq" 
   Use help("models", package = "emmeans") for information on supported models.
  You may try 'ggpredict()' or 'ggeffect()'.
  
  Can't compute marginal effects, 'effects::Effect()' returned an error.
  
  Reason: Invalid operation on a survival time
  You may try 'ggpredict()' or 'ggemmeans()'.
  
  Can't compute marginal effects, 'effects::Effect()' returned an error.
  
  Reason: Invalid operation on a survival time
  You may try 'ggpredict()' or 'ggemmeans()'.
  
  Error: Confidence intervals could not be computed.
  * Reason: "argument of length 0"
  * Source: 1:nrow(prediction_data)
  Error: Confidence intervals could not be computed.
  * Reason: "argument of length 0"
  * Source: 1:nrow(prediction_data)
  Error: Confidence intervals could not be computed.
  * Reason: "argument of length 0"
  * Source: 1:nrow(prediction_data)
  Error: Confidence intervals could not be computed.
  * Reason: "argument of length 0"
  * Source: 1:nrow(prediction_data)
  Error: Confidence intervals could not be computed.
  * Reason: "argument of length 0"
  * Source: 1:nrow(prediction_data)
  Error: Confidence intervals could not be computed.
  * Reason: "argument of length 0"
  * Source: 1:nrow(prediction_data)
  Error: Confidence intervals could not be computed.
  * Reason: "argument of length 0"
  * Source: 1:nrow(prediction_data)
  Error: Confidence intervals could not be computed.
  * Reason: "argument of length 0"
  * Source: 1:nrow(prediction_data)
  ══ testthat results  ═══════════════════════════════════════════════════════════
  [ OK: 131 | SKIPPED: 43 | WARNINGS: 719 | FAILED: 1 ]
  1. Failure: ggemmeans (@test-coxph.R#19) 
  
  Error: testthat unit tests failed
  Execution halted